标题： 基于空间感知查找表分解的轻量级快速实时图像增强 显示英文标题

标题： Lightweight and Fast Real-time Image Enhancement via Decomposition of the Spatial-aware Lookup Tables

摘要： 基于三维查找表（3D LUTs）的图像增强方法通过在顶点处插值预计算的值，有效地减少了模型大小和运行时间。 然而，由于3D LUT方法是逐点转换颜色值，缺乏空间信息，因此存在局限性。 尽管空间感知的3D LUT方法解决了这一限制，但它们引入了需要大量参数的附加模块，导致随着图像分辨率的增加而运行时间变长。 为了解决这个问题，我们提出了一种方法，通过关注表格的冗余部分来生成图像自适应的LUT。 我们的高效框架将3D LUT分解为低维LUT的线性和，并采用奇异值分解 (SVD)。 此外，我们增强了空间特征融合的模块，使其更加缓存高效。 广泛的实验结果表明，我们的模型在保持空间感知能力和性能的同时，有效减少了参数数量和运行时间。 显示英文摘要

摘要： The image enhancement methods based on 3D lookup tables (3D LUTs) efficiently reduce both model size and runtime by interpolating pre-calculated values at the vertices. However, the 3D LUT methods have a limitation due to their lack of spatial information, as they convert color values on a point-by-point basis. Although spatial-aware 3D LUT methods address this limitation, they introduce additional modules that require a substantial number of parameters, leading to increased runtime as image resolution increases. To address this issue, we propose a method for generating image-adaptive LUTs by focusing on the redundant parts of the tables. Our efficient framework decomposes a 3D LUT into a linear sum of low-dimensional LUTs and employs singular value decomposition (SVD). Furthermore, we enhance the modules for spatial feature fusion to be more cache-efficient. Extensive experimental results demonstrate that our model effectively decreases both the number of parameters and runtime while maintaining spatial awareness and performance.